Demands on availability and reliability of vacuum pumps in modern semiconductor manufacturing processes have been constantly increasing. It is the reason that the costs for failed wafer batches and lost production times are higher and higher as the size of the production wafer is larger and larger. Technical demands on the vacuum pumps for such modern semiconductor processes have been well pointed out by Bahnen and Kuhn [Reference 1: R. Bahnen and M Kuhn, “Increased reliability of dry pumps due to process related adaptation and pre-failure warning,” Vacuum, Vol. 44, No 5-7, pp. 709-712, 1993]: High reliability without unscheduled downtime, very low maintenance, high capability of pumping corrosive and reactive gas mixtures, high capability of pumping particles and sublimable gas mixtures, and low vibration and noise levels, etc. In order to satisfy those demands, a new dry pump for the modern semiconductor processes should provide the adaptation capability for the various process-dependent running conditions [Reference 1]. The adaptation to the different processes was shown to require the dedicated measurement and control of operation parameters such as temperatures and gas pressures inside the dry pump stages. Those process-related parameters are significant to check if the desired pump operation conditions are satisfied or not. In addition to the process-related parameters, the monitoring schemes of the pump operation-related ones (electrical power, cooling water, purge gas, wear of pump parts—bearings, seals, gear box, and motor) were also suggested by Bahnen and Kuhn [Reference 1] to avoid the risk of unscheduled downtime, in addition to the process adaptation of dry pumps. The warning and alarm level-based monitoring scheme for each operation parameter was suggested to avoid unexpected pump failures. But, any logical way of selecting all the warning and alarming levels of process-dependent and operation-related parameters are not proposed. Such threshold level selection is still a very challenging issue in the early detection of vacuum pump failure.
The threshold level-based monitoring has been widely recognized as a traditional technique for the failure protection of pumps [Reference 2: R. H. Greene and D. A. Casada, Detection of pump degradation, NUREG/CR-6089/ORNL-6765, Oak Ridge National Laboratory, 1995]. Wegerich et al [Reference 3: S. W. Wegerich, D. R. Bell and X. Xu, “Adaptive modeling of changed states in predictive condition monitoring,” WO 02/057856 A2, 2002; Reference 4: S. W. Wegerich, A. Wolosewicz and R. M Pipke, “Diagnostic systems and methods for predictive condition monitoring,” WO 02/086726 A1, 2002], however, pointed out the drawbacks of the senor output-based threshold warning and alarming schemes: “The traditional technique could not provide responses to gross changes in operational parameters of a process or machine, often failed to provide adequate warning to prevent unexpected shutdowns, equipment damage or catastrophic safety hazards.” In order to overcome such limit of the traditional technique, they suggested the use of the neural network-based parametric model adaptive to new operational states [Reference 3] and the model-based diagnostic systems for predictive condition monitoring [Reference 4]. The neural network model, as known in the previous study [Reference 5: Wan-Sup Cheung, “Identification, stabilization and control of nonlinear systems using the neural network-based parametric nonlinear modelling,” Ph.D. Thesis, University of Southampton, 1993] on the identification and control of dynamic systems, has the useful capability of interpolating a new state lying between trained data sets and extrapolating a neighboring state outside (but very near) the trained sets. Wegerich et al [Reference 3, Reference 4] exploited the interpolation and extrapolation capability of the trained neural network to estimate the current state of the process or machine in response to the measured values of sensor outputs. The estimated state values are subtracted from the actually measured sensor outputs to obtain the residual signals that are used to judge how the process or system deviates from the modeled state. Furthermore, these residuals are also used to generate the residual threshold alert, to perform the statistical test to check the shift of the process or system to a new operation condition, and to rebuild up a new training set for the shifted operation region. The suggested signal processing schemes of generating the alerts and adapting to the shifted operation region, including the construction of the new training set for the shifted operation region and their model learning process, are seen to require severe computation work and to accompany the inherent complexity of the suggested model-based diagnostic system. Such unrealistic computation load and implementation complexity of the suggested monitoring system has become unavoidable technical issues encountered in the pump monitoring systems for the modern semiconductor manufacturing processes. Consequently, a simple model adaptive to the pump operation conditions is significant in developing a pump monitoring system. This point has been one of main technical issues of this invention to be addressed later.
Instead of using the above parametric models adaptive to varying operation conditions of vacuum pumps with age, Ushiku et al [Reference 6: Y. Ushiku, T. Arikado, S. Samata, T. Nakao, and Y. Mikata, “Apparatus for predicting life of rotary machine, equipment using the same, method for predicting life and determining repair timing of the same,” U.S. Patent Application Publication, US2003/0009311 A1, 2003], Samata et al [Reference 7: S. Samata, Y. Ushiku, K. Ishii, and T. Nakao, “Method for diagnosing life of manufacturing equipment using rotary machine,” U.S. Patent Application Publication, US2003/0154052 A1, 2003; Reference 8: S. Samata, Y. Ushiku, T. Huruhata, T. Nakao, and K. Ishii, “Method for predicting life span of rotary machine used in manufacturing apparatus and life predicting system,” U.S. Patent Application Publication, US2003/01543997 A1, 2003] and Ishii et al [Reference 9: K. Ishii, T. Nakao, Y. Ushiku, and S. Samata, “Method for avoiding irregular shutoff of production equipment and system for irregular shutoff,” U.S. Patent Application Publication, US2003/0158705 A1, 2003] suggested the statistical analysis methods and the Mahalanobis distance-based analysis method to determine whether or not the currently measured time series data are deviated from the “reference” time series data set corresponding to the normal operation conditions. The statistical analysis methods are based on the second order statistical properties of sampled signals [Reference 10: J. S. Bendat A. G. Piersol, Random data: Analysis and measurement procedures, John Wiley & Sons: N.Y., 1985], such as the averaged values, standard deviations, and correlation functions. Because the use of the statistical properties makes sense only to the stationary processes, they have limited applicability to multiple load-dependent operation conditions required for the different products. It means that each reference time series data set corresponding to each load-dependent operation is required. A critical issue here is how to construct the data sets of load-dependent reference time series sufficient to cover the full range of normal operation conditions. Any effective way for constructing them is not yet proposed by Y. Ushiku et al [Reference 6], Samata et al [Reference 7, Reference 8] and Ishii et al [Reference 9]. To overcome the limited ability of detecting the abnormal running condition using the statistical analysis methods, they also considered the Mahalanobis distance analysis methods, as well known in the multi-variable statistics [Reference 11: W. H. Woodall, R. Koudelik, Z. G. Stoumbos, K. L. Tsui, S. B. Kim, C. P. Carvounis, “A review and analysis of the Mahalanobis-Taguchi system,” TECHNOMETRICS, Vol. 45, No. 1, pp. 1-14, 2003], for the quantitative analysis of the similarity between the current time series data and the reference ones. When the reference time series data include the full range of normal operation conditions, those evaluation quantities seem to be more effective than the second order statistics (mean and variance) methods used for the traditional trend monitoring systems. But, the time series of normal operation conditions for new or reconditioned vacuum pumps are available only at the very beginning of each designated process such that the reference data with the full range of normal operation conditions could be not obtained without the time-consuming data acquisition and signal processing jobs. Any realistic way of constructing such reference data set is not well understood even in the modern semiconductor manufacturing community. In real, a modern semiconductor fabrication unit requires multiple processes with such different operation conditions as varying camber pressures, gas flow rates, and different gas mixtures and properties. Those process-related properties and operation conditions of semiconductor manufacturers are very confidential such that they are very often inaccessible to the vacuum pump suppliers. It is very significant to note that a vacuum pump monitoring and diagnosis system for the modern semiconductor processes should have the capacity of self-adapting to multiple process conditions. Developing an active way of recognizing different process conditions and diagnosing their operational states is essential for the dry-pump monitoring and diagnosis system for the modern semiconductor processes. This invention is shown to provide a realistic solution to such technical issues later.